Regenerative medicine meets mathematical modelling: developing symbiotic relationships

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

In silico model development and optimization of in vitro lung cell population growth

Tissue engineering predominantly relies on trial and error in vitro and ex vivo experiments to develop protocols and bioreactors to generate functional tissues. As an alternative, in silico methods have the potential to significantly reduce the timelines and costs of experimental programs for tissue engineering. In this paper, we propose a methodology to formulate, select, calibrate, and test mathematical models to predict cell population growth as a function of the biochemical environment and to design optimal experimental protocols for model inference of in silico model parameters. We systematically combine methods from the experimental design, mathematical statistics, and optimization literature to develop unique and explainable mathematical models for cell population dynamics. The proposed methodology is applied to the development of this first published model for a population of the airway-relevant bronchio-alveolar epithelial (BEAS-2B) cell line as a function of the concentration of metabolic-related biochemical substrates. The resulting model is a system of ordinary differential equations that predict the temporal dynamics of BEAS-2B cell populations as a function of the initial seeded cell population and the glucose, oxygen, and lactate concentrations in the growth media, using seven parameters rigorously inferred from optimally designed in vitro experiments.

Exploiting in silico modelling to enhance translation of liver cell therapies from bench to bedside

Cell therapies are emerging as promising treatments for a range of liver diseases but translational bottlenecks still remain including: securing and assessing the safe and effective delivery of cells to the disease site; ensuring successful cell engraftment and function; and preventing immunogenic responses. Here we highlight three therapies, each utilising a different cell type, at different stages in their clinical translation journey: transplantation of multipotent mesenchymal stromal/signalling cells, hepatocytes and macrophages. To overcome bottlenecks impeding clinical progression, we advocate for wider use of mechanistic in silico modelling approaches. We discuss how in silico approaches, alongside complementary experimental approaches, can enhance our understanding of the mechanisms underlying successful cell delivery and engraftment. Furthermore, such combined theoretical-experimental approaches can be exploited to develop novel therapies, address safety and efficacy challenges, bridge the gap between in vitro and in vivo model systems, and compensate for the inherent differences between animal model systems and humans. We also highlight how in silico model development can result in fewer and more targeted in vivo experiments, thereby reducing preclinical costs and experimental animal numbers and potentially accelerating translation to the clinic. The development of biologically-accurate in silico models that capture the mechanisms underpinning the behaviour of these complex systems must be reinforced by quantitative methods to assess cell survival post-transplant, and we argue that non-invasive in vivo imaging strategies should be routinely integrated into transplant studies.

Capturing the unpredictability of stem cells

A new mathematical model that can be applied to both single-cell and bulk DNA sequencing data sheds light on the processes governing population dynamics in stem cells.

Emerging perspectives on 3D printed bioreactors for clinical translation of engineered and bioprinted tissue constructs

3D printed/bioprinted tissue constructs are utilized for the regeneration of damaged tissues and as in vitro models. Most of the fabricated 3D constructs fail to undergo functional maturation in conventional in vitro settings. There is a challenge to provide a suitable niche for the fabricated tissue constructs to undergo functional maturation. Bioreactors have emerged as a promising tool to enhance tissue maturation of the engineered constructs by providing physical/biological cues along with a controlled nutrient supply under dynamic in vitro conditions. Bioreactors provide an ambient microenvironment most appropriate for the development of functionally matured tissue constructs by promoting cell proliferation, differentiation, and maturation for transplantation and drug screening applications. Due to the huge cost and limited availability of commercial bioreactors, there is a need to develop strategies to make customized bioreactors. Additive manufacturing (AM) may be a viable tool to fabricate custom designed bioreactors with better efficiency and at low cost. In this review, we have extensively discussed the importance of bioreactors in functionalizing tissue engineered/3D bioprinted scaffolds for bone, cartilage, skeletal muscle, nerve, and vascular tissue. In addition, the importance and fabrication of customized 3D printed bioreactors for the maturation of tissue engineered constructs are discussed in detail. Finally, the current challenges and future perspectives in translating commercial and custom 3D printed bioreactors for clinical applications are outlined.

An in vitro agent-based modelling approach to optimization of culture medium for generating muscle cells

Methodologies for culturing muscle tissue are currently lacking in terms of quality and quantity of mature cells produced. We analyse images from in vitro experiments to quantify the effects of culture media composition on mouse-derived myoblast behaviour and myotube quality. Metrics of early indicators of cell quality were defined. Images of muscle cell differentiation reveal that altering culture media significantly affects quality indicators and myoblast migratory behaviours. To study the effects of early-stage cell behaviours on mature cell quality, metrics drawn from experimental images or inferred by approximate Bayesian computation (ABC) were applied as inputs to an agent-based model (ABM) of skeletal muscle cell differentiation with quality indicator metrics as outputs. Computational modelling was used to inform further in vitro experiments to predict the optimum media composition for culturing muscle cells. Our results suggest that myonuclei production in myotubes is inversely related to early-stage nuclei fusion index and that myonuclei density and spatial distribution are correlated with residence time of fusing myoblasts, the age at which myotube-myotube fusion ends and the repulsion force between myonuclei. Culture media with 5% serum was found to produce the optimum cell quality and to make muscle cells cultured in a neuron differentiation medium viable.

Mathematical modelling with Bayesian inference to quantitatively characterize therapeutic cell behaviour in nerve tissue engineering

Cellular engineered neural tissues have significant potential to improve peripheral nerve repair strategies. Traditional approaches depend on quantifying tissue behaviours using experiments in isolation, presenting a challenge for an overarching framework for tissue design. By comparison, mathematical cell-solute models benchmarked against experimental data enable computational experiments to be performed to test the role of biological/biophysical mechanisms, as well as to explore the impact of different design scenarios and thus accelerate the development of new treatment strategies. Such models generally consist of a set of continuous, coupled, partial differential equations relying on a number of parameters and functional forms. They necessitate dedicated in vitro experiments to be informed, which are seldom available and often involve small datasets with limited spatio-temporal resolution, generating uncertainties. We address this issue and propose a pipeline based on Bayesian inference enabling the derivation of experimentally informed cell-solute models describing therapeutic cell behaviour in nerve tissue engineering. We apply our pipeline to three relevant cell types and obtain models that can readily be used to simulate nerve repair scenarios and quantitatively compare therapeutic cells. Beyond parameter estimation, the proposed pipeline enables model selection as well as experiment utility quantification, aimed at improving both model formulation and experimental design.

Lifelike Transformative Materials for Biohybrid Implants: Inspired by Nature, Driven by Technology

Today's living world is enriched with a myriad of natural biological designs, shaped by billions of years of evolution. Unraveling the construction rules of living organisms offers the potential to create new materials and systems for biomedicine. From the close examination of living organisms, several concepts emerge: hierarchy, pattern repetition, adaptation, and irreducible complexity. All these aspects must be tackled to develop transformative materials with lifelike behavior. This perspective article highlights recent progress in the development of transformative biohybrid systems for applications in the fields of tissue regeneration and biomedicine. Advances in computational simulations and data-driven predictions are also discussed. These tools enable the virtual high-throughput screening of implant design and performance before committing to fabrication, thus reducing the development time and cost of biomimetic and biohybrid constructs. The ongoing progress of imaging methods also constitutes an essential part of this matter in order to validate the computation models and enable longitudinal monitoring. Finally, the current challenges of lifelike biohybrid materials, including reproducibility, ethical considerations, and translation, are discussed. Advances in the development of lifelike materials will open new biomedical horizons, where perhaps what is currently envisioned as science fiction will become a science-driven reality in the future.

In-silico and in-vitro morphometric analysis of intestinal organoids

Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average number of budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models.

Perspectives on optimizing local delivery of drugs to peripheral nerves using mathematical models

Drug therapies for treating peripheral nerve injury repair have shown significant promise in preclinical studies. Despite this, drug treatments are not used routinely clinically to treat patients with peripheral nerve injuries. Drugs delivered systemically are often associated with adverse effects to other tissues and organs; it remains challenging to predict the effective concentration needed at an injured nerve and the appropriate delivery strategy. Local drug delivery approaches are being developed to mitigate this, for example via injections or biomaterial-mediated release. We propose the integration of mathematical modeling into the development of local drug delivery protocols for peripheral nerve injury repair. Mathematical models have the potential to inform understanding of the different transport mechanisms at play, as well as quantitative predictions around the efficacy of individual local delivery protocols. We discuss existing approaches in the literature, including drawing from other research fields, and present a process for taking forward an integrated mathematical-experimental approach to accelerate local drug delivery approaches for peripheral nerve injury repair. This article is categorized under: Neurological Diseases > Molecular and Cellular Physiology Neurological Diseases > Computational Models Neurological Diseases > Biomedical Engineering.

Adverse events, side effects and complications in mesenchymal stromal cell-based therapies

Numerous clinical studies have shown a wide clinical potential of mesenchymal stromal cells (MSCs) application. However, recent experience has accumulated numerous reports of adverse events and side effects associated with MSCs therapy. Furthermore, the strategies and methods of MSCs therapy did not change significantly in recent decades despite the clinical impact and awareness of potential complications. An extended understanding of limitations could lead to a wider clinical implementation of safe cell therapies and avoid harmful approaches. Therefore, our objective was to summarize the possible negative effects observed during MSCs-based therapies. We were also aimed to discuss the risks caused by weaknesses in cell processing, including isolation, culturing, and storage. Cell processing and cell culture could dramatically influence cell population profile, change protein expression and cell differentiation paving the way for future negative effects. Long-term cell culture led to accumulation of chromosomal abnormalities. Overdosed antibiotics in culture media enhanced the risk of mycoplasma contamination. Clinical trials reported thromboembolism and fibrosis as the most common adverse events of MSCs therapy. Their delayed manifestation generally depends on the patient's individual phenotype and requires specific awareness during the clinical trials with obligatory inclusion in the patient' informed consents. Finally we prepared the safety checklist, recommended for clinical specialists before administration or planning of MSCs therapy.

Evaluation of Polysaccharide-Peptide Conjugates Containing the RGD Motif for Potential Use in Muscle Tissue Regeneration

New scaffold materials composed of biodegradable components are of great interest in regenerative medicine. These materials should be: stable, nontoxic, and biodegrade slowly and steadily, allowing the stable release of biodegradable and biologically active substances. We analyzed peptide-polysaccharide conjugates derived from peptides containing RGD motif (H-RGDS-OH (1), H-GRGDS-NH₂ (2), and cyclo(RGDfC) (3)) and polysaccharides as scaffolds to select the most appropriate biomaterials for application in regenerative medicine. Based on the results of MTT and Ki-67 assays, we can state that the conjugates containing calcium alginate and the ternary nonwoven material were the most supportive of muscle tissue regeneration. Scanning electron microscopy imaging and light microscopy studies with hematoxylin-eosin staining showed that C2C12 cells were able to interact with the tested peptide-polysaccharide conjugates. The release factor (Q) varied depending on both the peptide and the structure of the polysaccharide matrix. LDH, Alamarblue^®, Ki-67, and cell cycle assays indicated that peptides 1 and 2 were characterized by the best biological properties. Conjugates containing chitosan and the ternary polysaccharide nonwoven with peptide 1 exhibited very high antibacterial activity against Staphylococcus aureus and Klebsiella pneumoniae. Overall, the results of the study suggested that polysaccharide conjugates with peptides 1 and 2 can be potentially used in regenerative medicine.

An Appearance Data-Driven Model Visualizes Cell State and Predicts Mesenchymal Stem Cell Regenerative Capacity

Mesenchymal stem cells (MSCs) are widely used in treating various diseases. However, lack of a reliable evaluation approach to characterize the potency of MSCs has dampened their clinical applications. Here, a function-oriented mathematical model is established to evaluate and predict the regenerative capacity (RC) of MSCs. Processed by exhaustive testing, the model excavates four optimal fitted indices, including nucleus roundness, nucleus/cytoplasm ratio, side-scatter height, and ERK1/2 from the given index combinations. Notably, three of them except ERK1/2 are cell appearance-associated features. The predictive power of the model is validated via screening experiments of these indices by predicting the RC of newly enrolled and chemical inhibitor-treated MSCs. Further RNA-sequencing analysis reveals that cell appearance-based indices may serve as major indicators to visualize the results of integration-weighted signals in and out of cells and reflect MSC stemness. In general, this study proposes an appearance data-driven predictive model for the RC and stemness of MSCs.

Stem Cell Homing in Intrathecal Applications and Inspirations for Improvement Paths

A transplanted stem cell homing is a directed migration from the application site to the targeted tissue. Intrathecal application of stem cells is their direct delivery to cerebrospinal fluid, which defines the homing path from the point of injection to the brain. In the case of neurodegenerative diseases, this application method has the advantage of no blood–brain barrier restriction. However, the homing efficiency still needs improvement and homing mechanisms elucidation. Analysis of current research results on homing mechanisms in the light of intrathecal administration revealed a discrepancy between in vivo and in vitro results and a gap between preclinical and clinical research. Combining the existing research with novel insights from cutting-edge biochips, nano, and other technologies and computational models may bridge this gap faster.

Skeletal muscle differentiation of human iPSCs meets bioengineering strategies: perspectives and challenges

Although skeletal muscle repairs itself following small injuries, genetic diseases or severe damages may hamper its ability to do so. Induced pluripotent stem cells (iPSCs) can generate myogenic progenitors, but their use in combination with bioengineering strategies to modulate their phenotype has not been sufficiently investigated. This review highlights the potential of this combination aimed at pushing the boundaries of skeletal muscle tissue engineering. First, the overall organization and the key steps in the myogenic process occurring in vivo are described. Second, transgenic and non-transgenic approaches for the myogenic induction of human iPSCs are compared. Third, technologies to provide cells with biophysical stimuli, biomaterial cues, and biofabrication strategies are discussed in terms of recreating a biomimetic environment and thus helping to engineer a myogenic phenotype. The embryonic development process and the pro-myogenic role of the muscle-resident cell populations in co-cultures are also described, highlighting the possible clinical applications of iPSCs in the skeletal muscle tissue engineering field.

Implementing computational modeling in tissue engineering: where disciplines meet

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

An ECHO of Cartilage: In Silico Prediction of Combinatorial Treatments to Switch Between Transient and Permanent Cartilage Phenotypes With Ex Vivo Validation

A fundamental question in cartilage biology is: what determines the switch between permanent cartilage found in the articular joints and transient hypertrophic cartilage that functions as a template for bone? This switch is observed both in a subset of OA patients that develop osteophytes, as well as in cell-based tissue engineering strategies for joint repair. A thorough understanding of the mechanisms regulating cell fate provides opportunities for treatment of cartilage disease and tissue engineering strategies. The objective of this study was to understand the mechanisms that regulate the switch between permanent and transient cartilage using a computational model of chondrocytes, ECHO. To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. In ANIMO, we generated an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 proteins with 200 interactions. We called this model ECHO, for executable chondrocyte. Previously, we showed that ECHO could be used to characterize mechanisms of cell fate decisions. ECHO was first developed based on a Boolean model of growth plate. Here, we show how the growth plate Boolean model was translated to ANIMO and how we adapted the topology and parameters to generate an articular cartilage model. In ANIMO, many combinations of overactivation/knockout were tested that result in a switch between permanent cartilage (SOX9+) and transient, hypertrophic cartilage (RUNX2+). We used model checking to prioritize combination treatments for wet-lab validation. Three combinatorial treatments were chosen and tested on metatarsals from 1-day old rat pups that were treated for 6 days. We found that a combination of IGF1 with inhibition of ERK1/2 had a positive effect on cartilage formation and growth, whereas activation of DLX5 combined with inhibition of PKA had a negative effect on cartilage formation and growth and resulted in increased cartilage hypertrophy. We show that our model describes cartilage formation, and that model checking can aid in choosing and prioritizing combinatorial treatments that interfere with normal cartilage development. Here we show that combinatorial treatments induce changes in the zonal distribution of cartilage, indication possible switches in cell fate. This indicates that simulations in ECHO aid in describing pathologies in which switches between cell fates are observed, such as OA.

Modelling-informed cell-seeded nerve repair construct designs for treating peripheral nerve injuries

Millions of people worldwide are affected by peripheral nerve injuries (PNI), involving billions of dollars in healthcare costs. Common outcomes for patients include paralysis and loss of sensation, often leading to lifelong pain and disability. Engineered Neural Tissue (EngNT) is being developed as an alternative to the current treatments for large-gap PNIs that show underwhelming functional recovery in many cases. EngNT repair constructs are composed of a stabilised hydrogel cylinder, surrounded by a sheath of material, to mimic the properties of nerve tissue. The technology also enables the spatial seeding of therapeutic cells in the hydrogel to promote nerve regeneration. The identification of mechanisms leading to maximal nerve regeneration and to functional recovery is a central challenge in the design of EngNT repair constructs. Using in vivo experiments in isolation is costly and time-consuming, offering a limited insight on the mechanisms underlying the performance of a given repair construct. To bridge this gap, we derive a cell-solute model and apply it to the case of EngNT repair constructs seeded with therapeutic cells which produce vascular endothelial growth factor (VEGF) under low oxygen conditions to promote vascularisation in the construct. The model comprises a set of coupled non-linear diffusion-reaction equations describing the evolving cell population along with its interactions with oxygen and VEGF fields during the first 24h after transplant into the nerve injury site. This model allows us to evaluate a wide range of repair construct designs (e.g. cell-seeding strategy, sheath material, culture conditions), the idea being that designs performing well over a short timescale could be shortlisted for in vivo trials. In particular, our results suggest that seeding cells beyond a certain density threshold is detrimental regardless of the situation considered, opening new avenues for future nerve tissue engineering.